{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "path = \"C:\\\\stockdata\\\\economic\\\\\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "szzsdf = pd.read_excel(path + \"shangzhengzhishu.xls\", sheetname=\"szzs\")  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clilvdf = pd.read_csv(path + \"clilv2.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "clilvdf['date'] = pd.to_datetime(clilvdf['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g0Fqrfu3LtXhhzg9Nr1WbdsY4U0g5fouI24NCNFtGJMaPt9ubNnk7xy3OpVkz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\nREQQBEFIGBERQRAEIWFERARBEISE+f9TiFPsaEHpRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x884ec70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "plt.plot(szzsdf['date'], szzsdf['close'], label='szzs', color='red')\n",
    "# ax1.semilogy(szzsdf['date'],szzsdf['close'], color='red') #x轴为对数坐标轴\n",
    "ax2 = ax1.twinx()\n",
    "plt.plot(clilvdf['date'], clilvdf['lilv'], label='clilv')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
